Journal article
Sparse Tensor Additive Regression
Journal of machine learning research, Vol.22(1), pp.2989-3031
01/01/2021
Abstract
Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that models a scalar response as a flexible nonparametric function of tensor covariates. The proposed model effectively exploits the sparse and low-rank structures in the tensor additive regression. We formulate the parameter estimation as a non-convex optimization problem, and propose an efficient penalized alternating minimization algorithm. We establish a non-asymptotic error bound for the estimator obtained from each iteration of the proposed algorithm, which reveals an interplay between the optimization error and the statistical rate of convergence. We demonstrate the efficacy of STAR through extensive comparative simulation studies, and an application to the click-through-rate prediction in online advertising.
Details
- Title: Subtitle
- Sparse Tensor Additive Regression
- Creators
- Botao Hao - Google DeepMind (United Kingdom)Boxiang Wang - Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USAPengyuan Wang - University of GeorgiaJingfei Zhang - University of MiamiJian Yang - Verizon (United States)Will Wei Sun - Purdue University West Lafayette
- Resource Type
- Journal article
- Publication Details
- Journal of machine learning research, Vol.22(1), pp.2989-3031
- ISSN
- 1532-4435
- eISSN
- 1533-7928
- Publisher
- Microtome Publ
- Number of pages
- 43
- Grant note
- N00014-18-1-2759 / Office of Naval Research (ONR); Office of Naval Research DMS-2015190 / National Science Foundation (NSF); National Science Foundation (NSF); National Research Foundation of Korea
- Language
- English
- Date published
- 01/01/2021
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257740502771
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